Tool wear measurement in turning using force ratio

The aim of this work was to develop a reliable method to predict flank wear during the turning process. The present work developed a mathematical model for on-line monitoring of tool wear in a turning process. Force signals are highly sensitive carriers of information about the machining process and, hence, they are the best alternatives for monitoring tool wear. In the present work, determination of tool wear has been achieved by using force signals. The relationship between flank wear and the ratio of force components was established on the basis of data obtained from a series of experiments. Measurement of the ratio between the feed force and the cutting force components (F_f/F_c) has been found to provide a practical method for an in-process approach to the quantification of tool wear. A series of experiments was conducted to study the effects of tool wear as well as other cutting parameters on the cutting force signals, and to establish a relationship between the force signals, tool wear and other cutting parameters. The flank wear and the ratio of forces at different working conditions were collected experimentally to develop a mathematical model for predicting flank wear. The model was verified by comparing the experimental values with the predicted values. The relationship was then used for determination of tool flank wear.     

Tool flank wear prediction in CNC turning of 7075 AL alloy SiC composite

Flank wear occurs on the relief face of the tool and the life of a tool used in a machining process depends upon the amount of flank wear; so predicting of flank wear is an important requirement for higher productivity and product quality. In the present work, the effects of feed, depth of cut and cutting speed on flank wear of tungsten carbide and polycrystalline diamond (PCD) inserts in CNC turning of 7075 AL alloy with 10 wt% SiC composite are studied; also artificial neural network (ANN) and co-active neuro fuzzy inference system (CANFIS) are used to predict the flank wear of tungsten carbide and PCD inserts. The feed, depth of cut and cutting speed are selected as the input variables and artificial neural network and co-active neuro fuzzy inference system model are designed with two output variables. The comparison between the results of the presented models shows that the artificial neural network with the average relative prediction error of 1.03% for flank wear values of tungsten carbide inserts and 1.7% for flank wear values of PCD inserts is more accurate and can be utilized effectively for the prediction of flank wear in CNC turning of 7075 AL alloy SiC composite. It is also found that the tungsten carbide insert flank wear can be predicted with less error than PCD flank wear insert using ANN. With Regard to the effect of the cutting parameters on the flank wear, it is found that the increase of the feed, depth of cut and cutting speed increases the flank wear. Also the feed and depth of cut are the most effective parameters on the flank wear and the cutting speed has lesser effect.     

Mechanisms involved in the improvement of Inconel 718 machinability by laser assisted machining (LAM)

The aim of the present research work has been to gain a broader understanding of how or why laser assisted machining (LAM) improves machinability of Inconel 718, a hard-to-machine material of interest in the aeronautic industry. This has been accomplished by, first, running short run tests to determine the laser parameters and configuration for which highest force reductions are obtained and also to determine the effect of cutting parameters (feed, cutting speed and depth of cut) on force reduction. Secondly, long run tests have been performed in order to analyze process variables such as cutting forces, tool wear and surface roughness. Temperatures and hardness have been also measured in order to gain a broader perspective of the process.Experimental results have demonstrated that LAM improves machinability of Inconel 718 since machining forces and final surface roughness are reduced. The novelty reached with the present research work is the identification of three mechanisms associated to the laser heating as the responsible of this machinability improvement: material yield strength reduction, material base hardness reduction (only in precipitation hardened Inconel 718) and elimination of the work hardening generated in previous machining passes. The reduction of the work hardening leads also to a lower notch wear that limits the risk of sudden failure of the cutting tool and thus the wear mode is changed to flank wear, which leads to a controllable tool life and better surface roughness.     

A comparative study of the cutting forces in high speed machining of Ti–6Al–4V and Inconel 718 with a round cutting edge tool

Titanium alloy Ti–6Al–4V and nickel-based superalloy Inconel 718 have been widely employed in modern manufacturing. The published literature on high speed machining (HSM) of the two materials often involves different machining set-up, which makes it difficult to directly apply the research findings from one material to the other to select the most appropriate tool geometry and cutting conditions. A comparative experimental study of HSM of Ti–6Al–4V and Inconel 718 is conducted in this paper using the same machining set-up. The scope of this study is limited in high speed finish machining, where the tool edge geometry plays a significant role. The experimental set-up and the methods of measuring the cutting forces and the tool edge radius are introduced. A total of 40 orthogonal high speed tube-cutting tests were performed, involving five levels of cutting speeds and four levels of feed rates. Based on extensive experimental data, the similarities and differences between HSM of Ti–6Al–4V and Inconel 718 are quantitatively compared and qualitatively explained in terms of four quantities: (1) the cutting force F_c, (2) the thrust force F_t, (3) the resultant force R, and (4) the force ratio F_c/F_t. A total of 12 empirical regression relationships are obtained.     

Failure mechanisms of TiB2 particle and SiC whisker reinforced Al2O3 ceramic cutting tools when machining nickel-based alloys

In this paper, Al₂O₃/TiB₂/SiC_w ceramic cutting tools with different volume fraction of TiB₂ particles and SiC whiskers were produced by hot pressing. The fundamental properties of these composite tool materials were examined. Machining tests with these ceramic tools were carried out on the Inconel718 nickel-based alloys. The tool wear rates and the cutting temperature were measured. The failure mechanisms of these ceramic tools were investigated and correlated to their mechanical properties. Results showed that the fracture toughness and hardness of the composite tool materials continuously increased with increasing SiC whisker content up to 30 vol.%. The relative density decreased with increasing SiC whisker content, the trend of the flexural strength being the same as that of the relative density. Cutting speeds were found to have a profound effect on the wear behaviors of these ceramic tools. The ceramic tools exhibited relative small flank and crater wear at cutting speed lower than 100 m/min, within further increasing of the cutting speed the flank and crater wear increased greatly. Cutting speeds less than 100 m/min were proved to be the best range for this kind of ceramic tool when machining Inconel718 nickel-based alloys. The composite tool materials with higher SiC whisker content showed more wear resistance. Abrasive wear was found to be the predominant flank wear mechanism. While the mechanisms responsible for the crater wear were determined to be adhesion and diffusion due to the high cutting temperature.     

Indirect cutting force measurement in multi-axis simultaneous NC milling processes

There have been many research works for the indirect cutting force measurement in machining process, which deal with the case of one-axis cutting process. In multi-axis cutting process, the main difficulties to estimate the cutting forces occur when the feed direction is reversed. This paper presents the indirect cutting force measurement method in contour NC milling processes by using current signals of servo motors. A Kalman filter disturbance observer and an artificial neural network (ANN) system are suggested. A Kalman filter disturbance observer is implemented by using the dynamic model of the feed drive servo system, and each of the external load torques to the x and y-axis servo motors of a horizontal machining center is estimated. An ANN system is also implemented with a training set of experimental cutting data to measure cutting force indirectly. The input variables of the ANN system are the motor currents and the feedrates of x and y-axis servo motors, and output variable is the cutting force of each axis. A series of experimental works on the circular interpolated contour milling process with the path of a complete circle has been performed. It is concluded that by comparing the Kalman filter disturbance observer and the ANN system with a dynamometer measuring cutting force directly, the ANN system has a better performance.     

New observations on tool wear mechanism in dry machining Inconel718

Tool wear is a problem in machining nickel-based alloy Inconel718, and it is thus of great importance to understand tool wear. Tool wear mechanism in dry machining Inconel718 with coated cemented carbide tools was analyzed in this paper. CCD and scanning electron microscopy (SEM) equipped with energy dispersive X-ray spectrometer (EDS) were used to study tool wear mechanism. The results show that the main reason which causes cutting tool wear was that the tool materials fall off from the tool substrate in the form of wear debris. In addition,, element diffusion between tool and workpiece and oxidation reaction all accelerate the formation and the peeling of the wear debris. According to analysis of tool wear mechanism, tool flank wear model was established. The optimal temperature in machining Inconel718 with PVD-coated (TiAlN) tool was obtained through the established model. Excellent experimental agreement was achieved in optimal temperature calculated by the established model.     

An experimental study of tool wear and cutting force variation in the end milling of Inconel 718 with coated carbide inserts

Inconel 718 is a difficult-to-cut nickel-based superalloy commonly used in aerospace industry. This paper presents an experimental study of the tool wear propagation and cutting force variations in the end milling of Inconel 718 with coated carbide inserts. The experimental results showed that significant flank wear was the predominant failure mode affecting the tool life. The tool flank wear propagation in the up milling operations was more rapid than that in the down milling operations. The cutting force variation along with the tool wear propagation was also analysed. While the thermal effects could be a significant cause for the peak force variation within a single cutting pass, the tool wear propagation was believed to be responsible for the gradual increase of the mean peak force in successive cutting passes.     

Modeling of tool wear in drilling by statistical analysis and artificial neural network

The useful life of a cutting tool and its operating conditions largely control the economics of the machining operations. Hence, it is imperative that the condition of the cutting tool, particularly some indication as to when it requires changing, to be monitored. The drilling operation is frequently used as a preliminary step for many operations like boring, reaming and tapping, however, the operation itself is complex and demanding.

Back propagation neural networks were used for detection of drill wear. The neural network consisted of three layers input, hidden and output. Drill size, feed, spindle speed, torque, machining time and thrust force are given as inputs to the ANN and the flank wear was estimated. Drilling experiments with 8 mm drill size were performed by changing the cutting speed and feed at two different levels. The number of neurons in the hidden layer were selected from 1, 2, 3, …, 20. The learning rate was selected as 0.01 and no smoothing factor was used. The estimated values of tool wear were obtained by statistical analysis and by various neural network structures. Comparative analysis has been done between statistical analysis, neural network structures and the actual values of tool wear obtained by experimentation.     

镍基合金Inconel 718薄壁件铣削加工数控程序和切削参数优化

薄壁件加工过程因切削力波动较大可导致切削过程不平稳,需对加工工艺进行优化。建立了镍基合金Inconel718薄壁件铣削加工数控编程优化模型,模型由数控编程、材料数据库和数控加工仿真3个模块组成。在UG中建立工件实体模型,并生成相应NC加工代码;基于Power Law本构方程,考虑材料热力学动态性能和材料分离准则对切削力和切削温度的影响,采用有限元仿真软件AdvantEdge FEM获得镍基合金车削加工的切削力和切削温度等参数;将工件毛坯模型、NC加工代码、材料数据导入Production Module中,对加工过程进行优化。结果表明:利用优化后的数控程序进行加工,可减小切削力波动,有助于改善薄壁件加工过程中的稳定性。     

Some experimental investigations in the drilling of carbon fiber-reinforced plastic (CFRP) composite laminates

In this paper, a concept of delamination factor F_d (i.e. the ratio of the maximum diameter D_max in the damage zone to the hole diameter D) is proposed to analyze and compare easily the delamination degree in the drilling of carbon fiber-reinforced plastic (CFRP) composite laminates. Experiments were performed to investigate the variations of cutting forces with or without onset of delamination during the drilling operations. The effects of tool geometry and drilling parameters on cutting force variations in CFRP composite materials drilling were also experimentally examined. The experimental results show that the delamination-free drilling processes may be obtained by the proper selections of tool geometry and drilling parameters. The effects of drilling parameters and tool wear on delamination factor are also presented and discussed.Cutting temperature has long been recognized as an important factor influencing the tool wear rate and tool life. An experimental investigation of flank surface temperatures is also presented in this paper. Experimental results indicated that the flank surface temperatures increase with increasing cutting speed but decreasing feed rate. Optimal cutting conditions are proposed to avoid damage from burning during the drilling processes.     

Cutting temperature of ceramic tools in high speed machining of difficult-to-cut materials

This paper deals with an experimental and analytical investigation into the different factors which influence the temperature distribution on Al₂O₃---TiC ceramic tool rake face during machining of difficult-to-cut materials, such as case hardened AISI 1552 steel (60–65 Rc) and nickel-based superalloys (e.g. Inconel 718). The temperature distribution was predicted first using the finite element analysis. Temperature measurements on the tool rake face using a thermocouple based technique were performed and the results were verified using the finite element analysis. Experiments were then performed to study the effect of cutting parameters, different tool geometries, tool conditions, and workpiece materials on the cutting edge temperatures. Results presented in this paper indicate that for turning case hardened steel, increasing the cutting speed, feted, and depth of cut will increase the cutting edge temperature. On the other hand, increasing the tool nose radius, and angle of approach reduces the cutting edge temperature, while increasing the width of the tool chamfer will slightly increase the cutting ege temperature. As for the negative rake angle, it was found that there is an optimum value of rake angle where the cutting edge temperature was minimum. For the Inconel 718 material, it was found that the cutting edge temperature reached a minimum at a speed of 510 m/min, and feed of 1.25 mm/rev. However, the effect of the depth of cut and tool nose radius was almost the same as that determined in the turning of case hardened steel. It was also observed in turning Inconel 718 with ceramic tools that, cutting forces and different types of tool wear were reduced with increasing the feed.     

Development of flank wear prediction model of Zirconia Toughened Alumina (ZTA) cutting tool using response surface methodology

Flank wear is an important criterion for machinability assessment of a material. The present study is an attempt to evaluate the influence of factors such as cutting speed, feed rate and depth of cut on flank wear during hard turning of EN 24 steel with newly developed transformed toughened nano-composite Zirconia Toughened Alumina (ZTA) ceramic inserts. ZTA provides a cost effective materials solution to the most demanding applications which require wear resistance, corrosion resistance, high temperature stability and superior mechanical strength. Several machining experiments were performed and mathematical models for flank wear have been postulated by using Response Surface Methodology (RSM). The analysis was based on a first order model in which the flank wear (V_b) is expressed as a function of three independent variables i.e. cutting speed (V), feed rate (F) and depth of cut (T). Analysis of Variance (ANOVA) was applied to check the adequacy of the mathematical model and their respective parameters. Key parameters and their interactive effect on flank wears have also been presented in graphical contours which may help for choosing the process parameters and predict the cutting condition for maximum tool life.     

Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks

In machining of parts, surface quality is one of the most specified customer requirements. Major indication of surface quality on machined parts is surface roughness. Finish hard turning using Cubic Boron Nitride (CBN) tools allows manufacturers to simplify their processes and still achieve the desired surface roughness. There are various machining parameters have an effect on the surface roughness, but those effects have not been adequately quantified. In order for manufacturers to maximize their gains from utilizing finish hard turning, accurate predictive models for surface roughness and tool wear must be constructed. This paper utilizes neural network modeling to predict surface roughness and tool flank wear over the machining time for variety of cutting conditions in finish hard turning. Regression models are also developed in order to capture process specific parameters. A set of sparse experimental data for finish turning of hardened AISI 52100 steel obtained from literature and the experimental data obtained from performed experiments in finish turning of hardened AISI H-13 steel have been utilized. The data sets from measured surface roughness and tool flank wear were employed to train the neural network models. Trained neural network models were used in predicting surface roughness and tool flank wear for other cutting conditions. A comparison of neural network models with regression models is also carried out. Predictive neural network models are found to be capable of better predictions for surface roughness and tool flank wear within the range that they had been trained.Predictive neural network modeling is also extended to predict tool wear and surface roughness patterns seen in finish hard turning processes. Decrease in the feed rate resulted in better surface roughness but slightly faster tool wear development, and increasing cutting speed resulted in significant increase in tool wear development but resulted in better surface roughness. Increase in the workpiece hardness resulted in better surface roughness but higher tool wear. Overall, CBN inserts with honed edge geometry performed better both in terms of surface roughness and tool wear development.     

切削Inconel718时新型陶瓷刀具JX-2-Ⅰ的切削性能研究

JX－2－Ⅰ是最新研制的碳化硅晶须（SiCw）增韧和碳化硅颗粒（SiCp）弥散增强氧化铝（Al2O3）新型陶瓷刀具。本文详细研究了该刀具加工Inconel718时的切削性能，结果表明，在低速干切时的刀具抗磨损能力为YG8＞JX－2－Ⅰ＞JX－1＞JX－2－Ⅱ；在105m／min的高速湿式切削时，JX－2－Ⅰ的切削性能与JX－1差不多，但是在42m／min的速度时JX－2－Ⅰ的切削性能好于JX－1（Al2O3＋SiCw）。同时发现在用JX－2－Ⅰ中高速切削Inconel718时必须使用冷却液。由于切削温度对工件材料加工硬化的影响，以及对工件材料高温强度屈服拐点的影响而存在一个切削速度的最佳选取范围。SEM分析表明，刀具磨损的主要形式是后刀面磨损、边界磨损、切深沟槽磨损和前刀面月牙洼磨损；刀具磨损的主要机理是粘结磨损、磨粒磨损和塑性变形磨损。     

Monitoring of flank wear of coated tools in high speed machining with a neural network ART2

A monitoring system for classifying the levels of the tool flank wear of coated tools into some categories has been developed using an unsupervised and self-organizing artificial neural network, ART2. The input pattern used for the ART2 was an array of normalized mean wavelet coefficients of the feed force, which was affected by not only the flank wear but also the severe crater wear observed in high speed machining. The outputs of ART2 were classified into four or five categories of wear levels: the incipient stage, one or two intermediate stages, final stage and hazardous stage. For two apparently different series of input data obtained under the same cutting conditions, which are often experienced in the experiment, the ART2 neural network showed very similar classification of tool wear levels from the beginning to the end of cutting. Further study proved that this monitoring system detected the excessive wear in the hazardous stage for different cutting speeds 5–7 m/s and different feed rates 0.10–0.20 mm/rev.     

Tool wear mechanisms of PcBN in machining Inconel 718: Analysis across multiple length scale

Recently, PcBN tooling have been successfully introduced in machining Ni-based superalloys, yet our knowledge of involved wear mechanisms remains limited. In this study, an in-depth investigation of PcBN tool degradation and related wear mechanisms when machining Inconel 718 was performed. Diffusional dissolution of cBN is an active wear mechanism. At high cutting speed oxidation of cBN becomes equally important. Apart from degradation, tool protection phenomena were also discovered. Oxidation of Inconel 718 resulted in formation of γ-Al₂O₃ and (Al,Cr,Ti)₃O₄ spinel that were deposited on the tool rake. Also on the rake, formation of (Ti,Nb,Cr)N takes place due to cBN-workpiece interaction. This creates a sandwich tool protection layer forming continuously as tool wear progresses. Such in operando protection enabled counterbalancing tool wear mechanisms and achieved high performance of PcBN in machining.     

Prediction and control of surface roughness in CNC lathe using artificial neural network

In this study, a neural network approach is presented for the prediction and control of surface roughness in a computer numerically controlled (CNC) lathe. Experiments have been performed on the CNC lathe to obtain the data used for the training and testing of a neural network. The parameters used in the experiment were reduced to three cutting parameters which consisted of depth of cutting, cutting speed, and feed rate. Each of the other parameters such as tool nose radius, tool overhang, approach angle, workpiece length, workpiece diameter and workpiece material was taken as constant. A feed forward multi-layered neural network was developed and the network model was trained using the scaled conjugate gradient algorithm (SCGA), which is a type of back-propagation. The adaptive learning rate was used. Therefore, the learning rate was not selected before training and it was adjusted during training to minimize training time. The number of iterations was 8000 and no smoothing factor was used. R_a, R_z and R_max were modeled and were evaluated individually. One hidden layer was used for all models while the numbers of neurons in the hidden layer of the R_a model were five and the numbers of neurons in the hidden layers of the R_z and R_max models were ten. The results of the neural network approach were compared with actual values. In addition, inasmuch as the control of surface roughness is proposed, a control algorithm was developed in the present investigation. The desired surface roughness was entered into the control system as a reference value and the controller determined the cutting parameters for these surface roughness values. A new surface roughness value was determined by sending the cutting parameters to the observer (ANN block). The obtained surface roughness was fed back to the comparison unit and was compared with the reference value and the difference surface roughness was then sent to the controller. The iteration was continued until the difference was reduced to a certain value of surface roughness which could be permitted for machining accuracy. When the surface roughness reached the permitted value, these cutting parameters were sent to the CNC turning system as input values. In conclusion, both the surface roughness values corresponding to the cutting parameters and suitable cutting parameters for a certain surface roughness can be determined prior to a machining operation using the ANN and control algorithm.     

Influence of oxygen on the tool wear in machining

High temperatures generated in machining are known to facilitate oxidation wear. A controlled atmosphere chamber was developed to investigate the effects of oxygen on tool wear and high speed machining tests were conducted on air and in argon. Cemented carbide, cermet and cubic boron nitride tooling was used on alloyed steel, hardened tool steel and superalloy Alloy 718. Machining in argon resulted in higher flank wear, higher cutting forces, and larger tool–chip contact length on the rake face. However, in hard machining, argon atmosphere reduced rake cratering. Transmission electron microscopy of tools worn on air showed formation of nanocrystalline Al₂O₃ film on the rake when machining aluminium containing Alloy 718, while no oxide films was detectable in the other cases.     

Influence of machining cycle of horizontal milling on the quality of cutting force measurement for the cutting tool wear monitoring

The cutting tools are today used a lot by industry and they are expensive, so it was interesting to optimize their use, by developing a predictive method of their wear, particularly, the flank wear V _b . For this task, the flank tool wear was measured in off-line using a binocular microscope, whereas, the cutting forces are recorded by means of a dynamometer (Kistler 9255B). The acquired signatures are analyzed during the milling operation throughout the tool life. In this paper, we are interested in the extraction of the appropriate indicators which characterize the tool wear by temporal and frequential analyses of the cutting force signals; and highlighting the influence of the clamp holes and the machining cycle to the quality of the measurements.